Would you like to become a University Expert in Forecasting but don't have time to attend on-site classes? Then, you are before the perfect option to achieve it. Do you trust TECH?”

Human behavior, social trends, the results of a political campaign, the development of science, armed conflicts or the epidemiology of a disease are just a few activities in which Statistical Forecasting plays a fundamental role in the estimation of what will happen in the future of each of them and, therefore, in their evolution. Although it is not an exact science, probability, based on the existing conditions of a given context, is capable of establishing, with a minimum margin of error, the optimal plan of action to achieve the best results.

Anticipating what will happen based on the exhaustive study of the structural keys of a project has allowed millions of public and private entities to develop business, social and economic strategies thanks to which they have achieved success. For this reason, and in order to provide all those interested in this area with the information that will allow them to get up to date on the advances that have been made in multivariate statistics and advanced forecasting, TECH and its team of professionals have developed this very completePostgraduate diploma Through 540 hours of theoretical and practical education, graduates will be able to delve into the latest developments in the different linear estimation models, as well as the most innovative tools for their application in different current contexts. They will also work with the different scales, from nominal to interval or ratio scales, concluding with an exhaustive analysis of the multiple regression techniques, their characteristics and the advantages and disadvantages of their use in certain cases.

And to overcome all the requirements of the qualification, you will have 6 months to access, without schedules, to the Virtual Campus and to complete the 3 modules that it includes. In addition, you will be provided with additional high-quality material presented in different formats, so that you can delve in a personalized way into the aspects you consider most important or relevant for your professional development and performance. It is, therefore, a unique opportunity to specialize in the field of Statistical Forecastingthrough a 100% online educational experience that adapts to you, your needs and the most demanding requirements of today's labor market.

The best program on the current educational market to delve into the linear prediction methods thatare setting the trend in the field of Applied Statistics”

This Postgraduate diploma in Forecasting contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Applied Statistics
• The graphic, schematic and practical contents of the book provide technical and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will work with the most comprehensive and diverse information on multivariate statistical techniques, from nominal scale to binary logistic regression modeling”

The program includes, in its teaching staff, a team of professionals from the sector who bring to this program the experience of their work, in addition to recognized specialists from prestigious reference societies and universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts.

Do you fully understand the application of the properties of idempotent matrices? If you want to achieve it, enroll in thisPostgraduate diploma and you will find everything you need"

You will be able to delve into the techniques of stratified analysis in 2x2 tables, as well as the formulation of the problem in loglinear models through theoretical, practical and additional content"

TECH is a reference in the online education scene for the high quality of its programs, as well as for being a pioneer in the use of innovative methodological techniques, such as the learning process based on Relearning, which consists of reiterating the most important concepts throughout the syllabus so that the graduate can implement them to their knowledge in a natural and progressive way, without the need to invest extra hours in memorizing. In addition, each of its programs includes diverse additional material, thanks to which you can delve in a personalized way in the different aspects of the content, attending an educational experience adapted to the demands of all professionals.

You will have access to 540 hours of theoretical, practical and additional material with which you will be able to delve into the different sections of the syllabus in a personalized way and according to your needs”

Module 1.  Linear Prediction Methods

1.1. Simple Linear Regression Models

1.1.1. Introduction to Regression Models and Preliminary Steps in Simple Regression: Data Exploration
1.1.2. Models
1.1.3. Hypotheses
1.1.4. Parameters

1.2. Simple Linear Regression Estimation and Contrasts

1.2.1. Point Estimation of Model Parameters

1.2.1.1. Least Squares Method
1.2.1.2. Maximum Likelihood Estimators

1.2.2. Inference on Model Parameters under the Gauss-Markov Hypothesis

1.2.2.1. Intervals
1.2.2.2. Test

1.2.3. Confidence Interval for the Mean Response and Prediction Interval for New Observations
1.2.4. Simultaneous Inferences in Simple Regression
1.2.5. Confidence and Prediction Bands

1.3. Simple Linear Regression Models Diagnosis and Validation

1.3.1. Analysis of Variance (ANOVA) of Simple Regression Models
1.3.2. Model Diagnostics

1.3.2.1. Graphical Assessment of Linearity and Verification of the Hypotheses by Residuals Analysis
1.3.2.2. Linear Lack-of-Fit Test

1.4. Multiple Linear Regression Models

1.4.1. Data Exploration with Multidimensional Visualization Tools
1.4.2. Matrix Expression of Models and Coefficient Estimators
1.4.3. Interpreting Coefficients of Multiple Models

1.5. Multiple Linear Regression Estimation and Contrasts

1.5.1. Laws of Estimation for Coefficients, Predictions, and Residuals
1.5.2. Applying Properties of Idempotent Matrices
1.5.3. Inference in Multiple Linear Models
1.5.4. Anova Models

1.6. Multiple Linear Regression Models Diagnosis and Validation

1.6.1. "Ligatures" Test to Solve Linear Constraints on Coefficients

1.6.1.1. The Principle of Incremental Variability

1.6.2. Waste Analysis
1.6.3. Box-Cox Transformation

1.7. The Problem of Multicollinearity

1.7.1. Detection
1.7.2. Solutions

1.8. Polynomial Regression

1.8.1. Definition and Example
1.8.2. Matrix Form and Calculating Estimates
1.8.3. Interpretation
1.8.4. Alternative Approaches

1.9. Regression with Qualitative Variables

1.9.1. Dummy Variables in Regression
1.9.2. Interpreting Coefficients
1.9.3. Applications

1.10. Criteria for Models Selection

1.10.1. Mallows Cp Statistics
1.10.2. Model Cross Validation
1.10.3. Automatic Stepwise Selection

Module 2.Multivariate Statistical Techniques

2.1. Introduction
2.2. Nominal Scale

2.2.1. Measures of Association for 2x2 Tables

2.2.1.1. Phi Coefficient
2.2.1.2. Relative Risk
2.2.1.3. Cross-Product Ratio (Odds Ratio)

2.2.2. Measures of Association for IxJ Tables

2.2.2.1. Contingency Ratio
2.2.2.2. Cramer's V
2.2.2.3. Lambdas
2.2.2.4. Tau of Goodman and Kruskal
2.2.2.5. Uncertainty Coefficient

2.2.3. Kappa Coefficient

2.3. Ordinal Scale

2.3.1. Gamma Coefficients
2.3.2. Kendall's Tau-B and Tau-C
2.3.3. Sommers' D

2.4. Interval or Ratio Scale

2.4.1. Eta Coefficient
2.4.2. Pearson's and Spearman's Correlation Coefficients

2.5. Stratified Analysis in 2x2 Tables

2.5.1. Stratified Analysis
2.5.2. Stratified Analysis in 2x2 Tables

2.6. Problem Formulation in Log-linear Models

2.6.1. The Saturated Model for Two Variables
2.6.2. The General Saturated Model
2.6.3. Other Types of Models

2.7. The Saturated Model

2.7.1. Calculation of Effects
2.7.2. Goodness of Fit
2.7.3. Test of K effects
2.7.4. Partial Association Test

2.8. The Hierarchical Model

2.8.1. The Backward Method

2.9. Probit Response Models

2.9.1. Problem Formulation
2.9.2. Parameter Estimation
2.9.3. Chi-Square Goodness-of-Fit Test
2.9.4. Parallelism Test for Groups
2.9.5. Estimation of the Dose Required to Obtain a Given Response Ratio

2.10. Binary Logistic Regression

2.10.1. Problem Formulation
2.10.2. Qualitative Variables in Logistic Regression
2.10.3. Selection of Variables
2.10.4. Parameter Estimation
2.10.5. Goodness of Fit
2.10.6. Classification of Individuals
2.10.7. Prediction

Module 3. Advanced Forecasting Techniques

3.1. General Linear Regression Model

3.1.1. Definition
3.1.2. Properties
3.1.3. Examples

3.2. Partial Least Squares Regression

3.2.1. Definition
3.2.2. Properties
3.2.3. Examples

3.3. Principal Component Regression

3.3.1. Definition
3.3.2. Properties
3.3.3. Examples

3.4. RRR Regression

3.4.1. Definition
3.4.2. Properties
3.4.3. Examples

3.5. Ridge Regression

3.5.1. Definition
3.5.2. Properties
3.5.3. Examples

3.6. Lasso Regression

3.6.1. Definition
3.6.2. Properties
3.6.3. Examples

3.7. Elasticnet Regression

3.7.1. Definition
3.7.2. Properties
3.7.3. Examples

3.8. Non-Linear Prediction Models

3.8.1. Non-Linear Regression Models
3.8.2. Non-Linear Least Squares
3.8.3. Conversion to a Linear Model

3.9. Parameter Estimation in a Non-Linear System

3.9.1. Linearization
3.9.2. Other Parameter Estimation Methods
3.9.3. Initial Values
3.9.4. Computer Programs

3.10. Statistical Inference in Non-Linear Regression

3.10.1. Statistical Inference in Non-Linear Regression
3.10.2. Approximate Inference Validation
3.10.3. Examples

You will have theoretical and practical examples of each module, so that you can conceptualize both the information and the forecasting techniques and strategies that you will find in this Postgraduate diploma”